{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib 数据可视化基础\n",
    "## 一.pyplot基础语法和常用参数\n",
    "### 1.创建画布与创建子图\n",
    "### 2.创建并选中子图\n",
    "\n",
    "## 二.pyplot绘制图形的主要函数\n",
    "### 1.绘制折线图\n",
    "\n",
    "## 知识目标：掌握pyplot基础语法\n",
    "## 技能目标：创建子图并使用plot函数绘制折线图\n",
    "##                   掌握绘制图形的保存与展示\n",
    "## 重点：pyplot基础语法\n",
    "## 难点：plot函数绘制折线图\n",
    "=========================================================\n",
    "\n",
    "【知识储备】\n",
    "#Matplotlib是Python中非常优秀的数据可视化第三方库。Matplotlib库由各种可视化类构成，内部结构复杂。\n",
    "\n",
    "https://matplotlib.org/gallery.html\n",
    "#设计者Matlab想让用户简单的使用库中的各种类，而不关心里面的组成逻辑，就出现了matplotlib.pyplot。\n",
    "#matplotlib.pyplot 是绘制各类可视化图形的命令子库，相当于快捷方式。\n",
    "#如何引用？——import matplotlib.pyplot as plt（plt引入模块的别名）\n",
    "#绘图的流程：创建画布—创建子图—制作图形—美化图形—保存图形——显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例1：初识plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot([3,3,5,6,2])                      #用plot函数绘制二维图形                                       \n",
    "plt.ylabel('garde')                        #给y轴加标签'garde'\n",
    "plt.savefig('D:/pyData/例1',dpi=80)       #保存图形，dpi英寸像素点（质量） \n",
    "plt.show()                                 #显示图形。默认PNG格式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "思考:例1的数值在纵横坐标分别代表什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一.pyplot基础语法和常用参数\n",
    "### 1.pyplot基础语法\n",
    "\n",
    "第一步 创建画布：plt.figure\n",
    "\n",
    "第二步 创建子图：plt.subplot\n",
    "\n",
    "第三步 制作图形：plt.plot、plt.barh、plt.hist、plt.pie、plt.scatter、plt.boxplot……\n",
    "\n",
    "第四步 美化图形：plt.title、plt.xlabel、plt.ylabel、plt.legend、color、lines.linewidth、lines.linestyle、lines.marker、lines.markersize……\n",
    "\n",
    "第五步 保存图形：plt.savefig\n",
    "\n",
    "第六步 显示图形：plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 第一步 创建画布\n",
    "plt.figure语法：\n",
    "plt.figure(num=None,figsize=None,dpi=None,facecolor=None,edgecolor=None, frameon=True)\n",
    "#num：图像编号或名称，数字为编号 ，字符串为名称\n",
    "#figsize：指定figure的宽和高，单位为英寸；\n",
    "#dpi：参数指定绘图对象的分辨率，即每英寸多少个像素，缺省值为80，1英寸等于2.5cm\n",
    "#facecolor：背景颜色\n",
    "#edgecolor：边框颜色\n",
    "#frameon：是否显示边框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例2：创建一个空白画布\n",
    "fig=plt.figure(figsize=(5,5), facecolor = '#FF00FF')\n",
    "plt.savefig('D:/pyData/例2.png', facecolor=fig.get_facecolor())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 第二步 创建子图\n",
    "plt.subplot(nrows,ncols,plot_number)\n",
    "          #行  #列  #当前子图区\n",
    "例如：plt.subplot(3,2,4)、（324）   #将画布分为3行2列，取第4格的区域作为当前子图区进行图形的绘制。\n",
    "\n",
    "3行2列分布图：\n",
    "\n",
    "     1  2\n",
    "     3  4\n",
    "     5  6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例3：创建子图\n",
    "fig=plt.figure(figsize=(5,5), facecolor = 'r')\n",
    "plt.subplot(3,2,4)\n",
    "plt.plot([0,2,4,6,8],[3,1,4,5,2])                                   # 第三步 制作图形\n",
    "plt.xlabel('garde')                                                 # 第四步 美化图形\n",
    "plt.savefig('D:/pyData/例3.png', facecolor=fig.get_facecolor())     # 第五步 保存图形\n",
    "plt.show()                                                          # 第六步 显示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比较：例1 和例3的结果，plot(y)与plot(x，y)的区别？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***常见绘制图形函数及美化图形函数及参数见word文档"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "思考1：pyplot基础语法综合实例，要求如图（例4）\n",
    "提示：x取值为0-100自然数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nums = np.arange(0,101)\n",
    "fig = plt.figure()\n",
    "#图1\n",
    "plt.subplot(221)\n",
    "plt.plot(nums,nums)\n",
    "plt.title('正比例函数')\n",
    "plt.xticks((0,20,40,60,80,100))\n",
    "plt.yticks((0,20,40,60,80,100))\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(['y=x'])\n",
    "#图2\n",
    "plt.subplot(222)\n",
    "plt.plot(nums,-nums)\n",
    "plt.title('反比例函数')\n",
    "plt.xticks((0,20,40,60,80,100))\n",
    "plt.yticks((-100,-80,-60,-40,-20,0))\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(['y=-x'])\n",
    "#图3\n",
    "plt.subplot(212)\n",
    "plt.plot(nums,nums**2,'g--')\n",
    "plt.title('曲线函数')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(['y=x^2'])\n",
    "plt.savefig('D:/pyData/例子')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#例4：pyplot基础语法综合实例\n",
    "plt.rcParams['font.family']='SimHei'         #中文正常显示，'SimHei'为黑体，还可以设置'SimSun'为宋体，'KaiTi'为楷体……\n",
    "plt.rcParams['axes.unicode_minus']=False    #负号正常显示\n",
    "nums = np.arange(0,101)\n",
    "fig = plt.figure(figsize=(8,6),dpi=200)\n",
    "plt.subplot(221)\n",
    "plt.title('正比例函数')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.xlim((0,100))\n",
    "plt.ylim((0,100))\n",
    "plt.xticks([0,20,40,60,80,100])\n",
    "plt.yticks([0,20,40,60,80,100])\n",
    "plt.plot(nums,nums)\n",
    "plt.legend(['y=x'])\n",
    "plt.subplot(222)\n",
    "plt.title('反比例函数')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.xlim((0,100))\n",
    "plt.ylim((-100,0))\n",
    "plt.xticks([0,20,40,60,80,100])\n",
    "plt.yticks([-100,-80,-60,-40,-20,0])\n",
    "plt.plot(nums,-nums)\n",
    "plt.legend(['y=-x'])\n",
    "plt.subplot(212)\n",
    "plt.title('曲线函数')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.plot(nums,nums**2,'r-.')\n",
    "plt.legend(['y=x^2'])\n",
    "plt.savefig(('D:/pyData/例4.png'))\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "补充：rcParams\n",
    "\n",
    "#rc:  run configuration  运行配置\n",
    "#params： 参数\n",
    "\n",
    "pyplot使用rcParams来自定义图形的各种默认属性，称之为运行配置参数。通过更改参数来修改默认的属性，比如窗体大小、每英寸的点数、线条宽度、样式、字体等（详见word文档rcParams参数及属性）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二.pyplot绘制图形的主要函数\n",
    "### 1.绘制折线图\n",
    "折线图是一种将数据点按照顺序连接起来的图形。比较适合描述和比较多组数据随时间变化的趋势。\n",
    "\n",
    "plt.plot语法：\n",
    "plt.plot(x, y, format_string, **kwargs)\n",
    "#x:  X轴数据，列表或数组\n",
    "#y:  Y轴数据，列表或数组\n",
    "#format_string：控制曲线的格式字符串（颜色字符、风格字符、标记字符组成）\n",
    "#**kwargs：第二组或更多plt.plot(x, y, format_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例5：读取“烧烤店营业额”数据，使用折线图绘制该店全年营业额的变化，要求绿色虚线连接每月数据，并在每月的数据上使用圆点做标记。\n",
    "data1 = pd.read_excel('D:pyData\\烧烤店营业额.xlsx')\n",
    "plt.figure()\n",
    "plt.plot(data1['月份'],data1['营业额（万元）'],'g--o')\n",
    "plt.title('烧烤店2019年营业额趋势图')\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('营业额（万元）')\n",
    "plt.savefig('D:pyData\\例5')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]\n",
    "a.append(round(10.888,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10.89]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例6：P203 9-1 顾客每多买一件就给优惠1%\n",
    "basePrice=49   #进价\n",
    "salePrice=75   #零售价\n",
    "\n",
    "#定义一个函数，计算购买num个商品时的单价\n",
    "def compute(num):\n",
    "    return salePrice*(1-0.01*num)\n",
    "\n",
    "#生成4个列表\n",
    "numbers = list(range(1,31))  #用来存储顾客购买数量，题目要求每人最多可以购买30件\n",
    "earns = []                  #用来存储商场的盈利情况\n",
    "total = []                   #用来存储顾客消费总额\n",
    "saves = []                   #用来存储顾客节省的总金额\n",
    "for num in numbers:\n",
    "    perPrice = compute(num)                            #购买num个时的成交单价\n",
    "    earns.append(round(num*(perPrice-basePrice),2))    #（购买单价-进价）*数量=商场盈利，保留2位小数\n",
    "    total.append(round(num*perPrice,2))                # 购买单价*数量 = 顾客消费总额，保留2位小数\n",
    "    saves.append(round(num*(salePrice-perPrice),2))    # （零售价-购买单价）*数量 = 节省的总金额，保留2位小数\n",
    "\n",
    "#绘制图形\n",
    "plt.plot(numbers,earns)     \n",
    "plt.plot(numbers,total)\n",
    "plt.plot(numbers,saves)\n",
    "plt.xlabel('顾客购买数据（件）')\n",
    "plt.ylabel('金额（元）')\n",
    "plt.title('利润最大化')\n",
    "plt.legend(['商家盈利','顾客总消费','顾客节省'])\n",
    "\n",
    "#绘制\n",
    "maxEarn = max(earns)\n",
    "bestNumber = numbers[earns.index(maxEarn)]\n",
    "plt.scatter([bestNumber],[maxEarn],marker='*',color='r',s=120)\n",
    "plt.text(bestNumber-1,maxEarn+200,str(maxEarn),fontsize=15)  \n",
    "q\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers[earns.index(max(earns))]   (17,225.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.绘制散点图\n",
    "散点图又称散点分布图，是以一个特征为横坐标，另一个特征为纵坐标，利用坐标点的分布形态反映特征间的统计关系。通常适合用于比较垮类别的数据。\n",
    "\n",
    "plt.scatter语法：\n",
    "plt.scatter (x, y, s, c, marker, alpha   )\n",
    "#x,y     表示xy轴对应的数据\n",
    "#s      表示点的大小\n",
    "#c      表示点的颜色\n",
    "#marker  表示点的形状\n",
    "#alpha   表示点的透明度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例7：使用散点图绘制随机正态分布小数\n",
    "x=np.random.normal(0,1,100)         #均值为0，标准差为1的随机正态分布数，一共100个\n",
    "y=np.random.normal(0,1,100)\n",
    "plt.scatter(x,y,c='y',marker='v')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>信号强度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>85</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>82</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>28</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>150</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>15</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>60</td>\n",
       "      <td>15</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>90</td>\n",
       "      <td>8</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>92</td>\n",
       "      <td>12</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>73</td>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "      <td>25</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>70</td>\n",
       "      <td>19</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>118</td>\n",
       "      <td>26</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>16</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>130</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>115</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x   y  信号强度\n",
       "0     0   0    60\n",
       "1     0   3    70\n",
       "2     5   0    68\n",
       "3    10   0    73\n",
       "4     0  30    56\n",
       "5     0  15    80\n",
       "6     1   6    85\n",
       "7     5  23    30\n",
       "8    25  30    20\n",
       "9    30   5    90\n",
       "10   50  25    95\n",
       "11   80   0    20\n",
       "12   85   5    24\n",
       "13   83  19    72\n",
       "14   82  28    17\n",
       "15  148   0    10\n",
       "16  140  28    53\n",
       "17  150  15    50\n",
       "18  100   1    30\n",
       "19   99  27    32\n",
       "20   70  15    95\n",
       "21   60  15   100\n",
       "22  120  16   100\n",
       "23   90   8    89\n",
       "24   92  12    91\n",
       "25   73   4    89\n",
       "26   72  25    86\n",
       "27   70  19    91\n",
       "28  118  26    83\n",
       "29   80  10    90\n",
       "30   38  16   100\n",
       "31   25  25    30\n",
       "32   40  28    70\n",
       "33  120   3    34\n",
       "34  130   5    45\n",
       "35  125   2    20\n",
       "36  115   2    20"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = pd.read_csv('D:pyData/商场一楼手机信号强度.txt')\n",
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = data3[data3['信号强度']>70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "b1 = data3[data3['信号强度']<40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = data3[(data3['信号强度']>40) & (data3['信号强度']<70)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(a1['x'],a1['y'])\n",
    "plt.scatter(b1['x'],b1['y'])\n",
    "plt.scatter(c1['x'],c1['y'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例8：P206 9-4\n",
    "data3 = pd.read_csv('D:pyData/商场一楼手机信号强度.txt')\n",
    "for x,y,s in zip(data3['x'],data3['y'],data3['信号强度']):\n",
    "    if s < 40:\n",
    "        color = 'r'\n",
    "    elif s <70:\n",
    "        color = 'b'\n",
    "    else:\n",
    "        color = 'g'\n",
    "    plt.scatter(x,y,s=s,c=color,marker='*')\n",
    "plt.title('商场内信号强度')\n",
    "plt.xlabel('长度坐标')\n",
    "plt.ylabel('宽度坐标')\n",
    "plt.savefig('D:pyData\\例8')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 4, 7)\n",
      "(2, 5, 8)\n",
      "(3, 6, 9)\n"
     ]
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b = [4,5,6]\n",
    "c = [7,8,9]\n",
    "item = zip(a,b,c)\n",
    "for i in item:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.绘制饼图\n",
    "饼图是将各项数据与其总和的比例显示在一张“饼”中，以“饼”的大小来确定每一项的占比，清楚反应部分与部分、部分与整体之间的关系。\n",
    "plt.pie语法：\n",
    "plt.pie(x,explode=None,labels=None,autopct=None,startangle=None)\n",
    "#x：           饼块的各个数据\n",
    "#explode：     饼块离中心的距离\n",
    "#labels：      饼块的标签\n",
    "#autopct：    饼块中百分比数值的设置\n",
    "#shadow：      饼图的阴影显示\n",
    "#startangle：  起始绘图的角度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例9：模拟电商市场占有率\n",
    "names =['淘宝','京东','唯品会','天猫']\n",
    "sizes =[1143,398,159,547]\n",
    "ex=(0,0,0,0.1)\n",
    "plt.pie(sizes,explode=ex,labels=names,autopct='%1.1f%%',shadow=False,startangle=90)\n",
    "plt.title('模拟2019年电商市场占有率')\n",
    "plt.savefig('D:/pyData/例9')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
